NEURO MODELLING AND CONTROL OF AN AUTOMOTIVE AIR CONDITIONING SYSTEM WONG KAM KHUAN

Transcription

1 NEURO MODELLING AND CONTROL OF AN AUTOMOTIVE AIR CONDITIONING SYSTEM WONG KAM KHUAN A project report submitted in partial fulfilment of the requirements for the award of the degree of Master of Engineering (Mechanical) Faculty of Mechanical Engineering Universiti Teknologi Malaysia JUNE 2014

2 To my beloved wife iii

3 iv ACKNOWLEDGEMENT In the preparation of this project dissertation, I was in contact with supervisors, researchers, academicians, practitioners and friends. They have contributed towards my understanding and thoughts. I would like to express my most sincere appreciation to my project supervisor, Assoc Prof Dr. Intan Zaurah Mat Darus, for guidance, advices, encouragement, motivation and friendship. I also would like to express my most sincere gratitude to my wife for supporting and loving me unconditionally. His assistance and supports are very helpful and useful for completing my project. My fellow postgraduate schoolmates and my best friend Mr. Sam Lim should also be appreciated for their helpful assistance and supports. Thank you very much.

4 v ABSTRACT Air Conditioning system is very important in our world nowadays. Air conditioning system provide the temperature comfort not only in building but also inside car. The objective of this study is to identify the dynamic model for an Automotive Air Conditioning (AAC) system by using Recursive Least Square (Traditional Method) and Artificial Neural Network (Intelligent Method). Beside that, both model are also to design a self tuning PID controller for variable speed of AAC compressor system by using Matlab-SIMULINK software. In the project, system identification techniques (Traditional Method) namely Recursive Least Square (RLS) and Artificial Neural Network (Intelligent Method) was use do estimate dynamic model of AAC system. The input and output of the data used to estimate the dynamic model of AAC system were obtained from the experimental. The system identification techniques were used with (ARX) model structure and the Intelligent techniques were used with (NARX) model structure. The validation of the RLS and ANN models were based on the Mean Square Error (MSE). Comparative performance of the RLS and ANN model in this project were discussed. From the discussion, it found that RLS model with order number of 2 give a better performance with minimum value of MSE. For the ANN model, number of neuron in hidden layer of 10 and delayed input/output data of 6 have been optimized as the best model with the minimum value of MSE. After that, the best model of RLS and ANN are used with PID controllers. The PID controllers are tuned using Auto tuned and heuristic tuning method. From this research, it found that, RLS model using PID controller tuned using Heuristic tuning method had shown the best performance, but on other hand, for the ANN model it was shown that Auto tuned PID have a better performance in comparison to Heuristic tuned PID.

5 vi ABSTRAK Sistem hawa dingin adalah sangat penting di dalam kehidupan di dunia ini. Sistem hawa dingin digunakan untuk menyediakan keselesaan bukan sahaja untuk bangunaan tetapi untuk kereta. Tujuan kajian ini adalah untuk menganggarkan model dinamik sistem hawa dingin automotif (AAC) dengan menggunakan teknik pengenalan sistem Least Square (LS) secara tradisional dan Artificial Neural Network (ANN) cara moden. Daripada ini, kedua-dua model ini juga direka dengan self pengawal PID kawalan untuk kawalan kelajuan AAC kompressor dengan Matlab-SIMULINK. Dalam projeck ini, teknik pengenalan sistem iaitu Recursive Least Square (Tradisional Cara) dan Artificial Neural Network (Moden Cara) digunakan untuk menganggarkan model dinamik sistem AAC. Data masukan dan keluaran yang digunakan untuk menganggar model dinamik system AAC diperolehi secara ujikaji darapada kajian. Sebelum ini teknik pengenalan sistem digunakan untuk (ARX) model struktur dan moden teknik digunakan untutk (NARX) model struktur. Kesahilan model disiasat berdasarkan Mean Square Error (MSE), keputusan di antara LS dan ANN yang telah dibangunkan dalam penyelidikan ini telah dibentangkan dan dibincangkan. Dalam perbincangan, didapati bahawa RLS dengan nombor pesanan 2 adalah pemodelan berparameter yang terbaik dengan MSE yang paling rendah. Untuk ANN model, lapisan tersembunyi 10 dan ditangguhkan 6 adalah pemodelan berparameter yang terbaik dengan MSE yang paling terrendah. Dengan model terbaik, gunakan cara Auto Tuned PID kawalan dan Heuristic PID kawalan untuk mengawal RLS model dan ANN model. Dalam kajian tersebut, RLS model dikawal dengan Heuristic Tuned PID kawalan memberikan pretasi terbaik. Manakala ANN model dikawal oleh Auto Tuned PID kawalan akan memberikan prestasi terbaik berbanding dengan Heuristic Tuned PID kawalan.